Facial Privacy Protection for Remote Photoplethysmography

Remote photoplethysmography (rPPG) has emerged as a crucial technology for contactless health monitoring, providing a convenient and non-invasive method to measure physiological signals from skin videos. Because face videos are commonly used for rPPG measurements, privacy concerns arise due to the inherent sensitivity of facial biometric data. Concerns about privacy breaches in facial video recordings have hindered telemedicine advancements and limited the creation of large-scale medical datasets, restricting the development of rPPG-based technologies. Additionally, the necessity to transmit and store rPPG videos in these applications necessitates video compression as an indispensable step. However, existing facial privacy protection techniques and video compression methods tend to degrade the rPPG signal in videos. To address these challenges, this study proposes a straightforward yet effective face anonymization module-a plug-and-play component employing spatial pixel redistribution algorithms to achieve: 1) eliminating identifiable biometric features while preserving the physiological information; 2) facilitating video compression by a macroblock reassembly strategy based on chromaticity clustering. Experiments on three rPPG datasets illustrate that the proposed method preserves physiological information in anonymized videos while effectively facilitating video compression.